Agglomerative clustering and collectiveness measure via exponent generating function

REN Wei-Ya, LI Shuo-Hao, Guo Qiang; LI Guo-Hui; Zhang Jun Email: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]. (College of Information System and Management, National University of Defense Technology, Hunan Changsha, 410073, China) Abstract: The key in agglomerative clustering is to define the affinity measure between two sets. A novel agglomerative clustering method is proposed by utilizing the path integral to define the affinity measure. Firstly, the path integral descriptor of an edge, a node and a set is computed by path integral and exponent generating function. Then, the affinity measure between two sets is obtained by path integral descriptor of sets. Several good properties of the path integral descriptor is proposed in this paper. In addition, we give the physical interpretation of the proposed path integral descriptor of a set. The proposed path integral descriptor of a set can be regard as the collectiveness measure of a set, which can be a moving system such as human crowd, sheep herd and so on. Self-driven particle (SDP) model is used to test the ability of the proposed method in measuring collectiveness.